Psychophysical studies frequently characterize human global motion perception in terms of the statistical properties of stimuli (e.g. vector average, mode or median of local directions). Conversely physiological research has investigated the computational principles (e.g. vector average, winner-take-all or maximum likelihood) by which cortical decision networks could read from the neural representation in the middle temporal area to guide the perceived direction of global motion. To reconcile these stimulus-based and mechanism-based approaches, we compared the ability of statistical measures of stimulus central tendency and algorithms based on the trial-by-trial responses of directionally-tuned mechanisms to predict perceived global direction. In a series of experiments, using the method of constant stimuli, human observers discriminated the global directions of two sequentially presented random-dot-kinematograms (RDKs). Each RDK (diameter 12 deg) contained 226 dots, drifting at 5 deg/s for 0.53 s. The dots of the standard RDK all moved in the same direction, randomly assigned on each trial. The direction of each dot of the comparison RDK was chosen independently from a skewed (asymmetric) probability distribution (either Gaussian or rectangular) with distinct measures of central tendency. The perceived global direction of the comparison RDK was derived from the point of subjective equality of the resulting psychometric functions. Results showed that none of the statistical measures of image direction central tendency was a consistently accurate predictor of perceived global motion direction. However regardless of the local composition of motion directions both maximum likelihood and winner-take-all (but not vector average) models, applied to the activity of a population of physiologically-plausible motion mechanisms, produced global motion estimates commensurate with the psychophysical data. This suggests that mechanism-based, read-out algorithms offer a more accurate and robust guide to human motion perception than any stimulus-based, statistical estimate of central tendency.